Fix bug

modules/maskrcnn_train.sh

@@ -39,52 +39,55 @@ if [ ! -f "$TRAIN_NET_FILE" ]; then
     echo "Downloading train_net.py file..."
     wget $TRAIN_NET_URL -O $TRAIN_NET_FILE
 fi
-
-# Function to extract the number of classes from COCO annotation
-NUM_CLASSES=$(python3 - <<END
-import json
-
-# Load the COCO annotation file
-with open("$TRAIN_ANNOTATION", 'r') as f:
-    coco_data = json.load(f)
-
-# Extract the number of unique categories
-num_classes = len(coco_data['categories'])
-print(num_classes)
-END
-)
-
-echo "Detected $NUM_CLASSES classes from the dataset."
-
-# Register the datasets with Detectron2 (assuming Detectron2 environment is set up)
+# Function to extract the number of classes from COCO annotation and run training
 python3 - <<END
 import os
+import json
 from detectron2.data.datasets import register_coco_instances
+from detectron2.data import DatasetCatalog
 
-# Register COCO datasets
+# Paths
 train_annotation = "$TRAIN_ANNOTATION"
 train_image_dir = "$TRAIN_IMAGE_DIR"
 val_annotation = "$VAL_ANNOTATION"
 val_image_dir = "$VAL_IMAGE_DIR"
 
-# Register with the name coco_roboone_train and coco_roboone_val
+# Load the COCO annotation file to detect number of classes
+with open(train_annotation, 'r') as f:
+    coco_data = json.load(f)
+
+# Extract number of unique categories
+num_classes = len(coco_data['categories'])
+print(f"Detected {num_classes} classes from the dataset.")
+
+# Register the datasets
 register_coco_instances("coco_roboone_train", {}, train_annotation, train_image_dir)
 register_coco_instances("coco_roboone_val", {}, val_annotation, val_image_dir)
 
+# Confirm the datasets are registered
 print("Datasets registered successfully.")
+print("Available datasets:", DatasetCatalog.list())
+
+# Import necessary modules for training
+from detectron2.engine import DefaultTrainer
+from detectron2.config import get_cfg
+
+# Set up configuration
+cfg = get_cfg()
+cfg.merge_from_file("$COCO_CONFIG_FILE")
+cfg.DATASETS.TRAIN = ("coco_roboone_train",)
+cfg.DATASETS.TEST = ("coco_roboone_val",)
+cfg.SOLVER.IMS_PER_BATCH = 2
+cfg.SOLVER.BASE_LR = 0.00025
+cfg.SOLVER.MAX_ITER = 1000
+cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
+cfg.MODEL.ROI_HEADS.NUM_CLASSES = num_classes  # Automatically set based on dataset
+cfg.OUTPUT_DIR = "$OUTPUT_DIR"
+
+# Train the model
+trainer = DefaultTrainer(cfg)
+trainer.resume_or_load(resume=False)
+trainer.train()
+
+print("Mask R-CNN training completed.")
 END
-
-# Run the training with Detectron2
-python3 $TRAIN_NET_FILE \
-  --config-file $COCO_CONFIG_FILE \
-  --num-gpus 1 \
-  OUTPUT_DIR $OUTPUT_DIR \
-  DATASETS.TRAIN "('coco_roboone_train',)" \
-  DATASETS.TEST "('coco_roboone_val',)" \
-  SOLVER.IMS_PER_BATCH 2 \
-  SOLVER.BASE_LR 0.00025 \
-  SOLVER.MAX_ITER 1000 \
-  MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE 512 \
-  MODEL.ROI_HEADS.NUM_CLASSES $NUM_CLASSES  # Automatically set based on dataset
-
-echo "Mask R-CNN training completed."